Graphics Multimedia

Designing Scientific Applications on GPUs by Raphael Couturier

By Raphael Couturier

Many of today’s complicated clinical purposes now require an unlimited volume of computational energy. normal goal portraits processing devices (GPGPUs) permit researchers in a number of fields to learn from the computational energy of all of the cores on hand within pictures cards.

Understand some great benefits of utilizing GPUs for lots of clinical Applications

Designing medical purposes on GPUs indicates you ways to exploit GPUs for functions in varied clinical fields, from physics and arithmetic to laptop technological know-how. The e-book explains the equipment worthy for designing or porting your clinical software on GPUs. it is going to increase your wisdom approximately photograph processing, numerical purposes, technique to layout effective purposes, optimization tools, and lots more and plenty more.

Everything you must Design/Port Your clinical software on GPUs

The first a part of the booklet introduces the GPUs and Nvidia’s CUDA programming version, at the moment the main common atmosphere for designing GPU purposes. the second one half makes a speciality of major photo processing purposes on GPUs. The 3rd half offers basic methodologies for software program improvement on GPUs and the fourth half describes using GPUs for addressing numerous optimization difficulties. The 5th half covers many numerical purposes, together with main issue difficulties, fluid simulation, and atomic physics versions. The final half illustrates agent-based simulations, pseudorandom quantity iteration, and the answer of enormous sparse linear structures for integer factorization. a number of the codes provided within the ebook can be found online.

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Petersona, and J. Dongarra. From CUDA to OpenCL: Towards a performance-portable solution for multi-platform GPU programming. Parallel Computing, 38(8):391–407, 2012. [3] NVIDIA Corporation. NVIDIA CUDA C Programming Guide, 2011. 0. 5 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . First example . . . . . . . . . . . . . . . . . . . . . . . . . . Second example: using CUBLAS . . . . . . . . . . . . . . . . Third example: matrix-matrix multiplication .

31 32 32 33 35 37 37 38 39 42 44 44 47 51 Introduction Median filtering is a well-known method used in a wide range of application frameworks as well as a standalone filter especially for salt and pepper denoising. It is able to greatly reduce the power of noise without blurring edges too much. That is actually why we originally focused on this filtering technique as a preprocessing stage when we were in the process of designing a GPU implementation of one region-based image segmentation algorithm [8].

2 shows for example that two 5 × 5 windows, centered on two neighbor pixels share at least 16 pixels. On a C2070 card, thanks to a more efficient caching mechanism, this effect is less. On GPUs, dependency on image size is low, and due to slightly more efficient data transfers when copying larger data amounts, pixel throughputs increases with image size. 5 MBytes) 64 times. 2. Illustration of window overlapping in 5x5 median filtering. 1. generic CUDA kernel achieving median filtering global void k e r n e l m e d i a n R ( short ∗ output , int i dim , int j dim , int r ) { 5 10 15 20 // absolute coordinates o f the center p i x e l int j = m u l 2 4 ( b l o c k I d x .

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